Performance Expert

This is the performance toolkit you reach for when things are slow and you need to figure out why. It covers profiling with cProfile, fixing N+1 queries with eager loading, setting up proper database connection pooling, and frontend patterns like debouncing and virtual scrolling. The examples are practical: memoization with lru_cache, batch database operations, Redis caching strategies, and switching from Python loops to NumPy vectorization. It's language-agnostic in scope but Python-heavy in implementation. Good for when you're past the "just throw hardware at it" phase and need to actually measure and optimize. The breadth is both its strength and weakness, covering everything from algorithm optimization to infrastructure tuning without going too deep on any single area.